A number of different groups require healthcare providers to extract and provide metrics on quality of care. Such groups include JCAHO, CMS, Leapfrog, and other organizations. Participation with most of these groups is voluntary, but may lead to reimbursement changes as pay-for-performance is implemented in healthcare. Furthermore, some of these metrics may become mandatory. Table 1 below is a list of common metrics:
NumberofReportingIndicatorsInitiativeIntervalby ProjectPopulationComparatorHQA (NVHRI)RollingAMI-5100% All PayerNationalYearCHF-2CAP-3RHQDAPUQuarterlyAMI-5100% All PayerNational(MarketCHF-2Basket)CAP-3NJ DHSSQuarterlyAMI-9100% All PayerStateCAP-6JCAHOQuarterlyAMI-11100% All PayerState andCoreCHF-7NationalMeasuresCAP-4PRO SOWQuarterlyAMI-8100% MedicareStateCHF-8CAP-4SIP-3CMSQuarterlyAMI-9100% PayerNationalDemonstrationCHF-4(292CAP-7Hospitals)CABG-8100% MedicareHip/Knee-6NationalRolling27100% All PayerAs DirectedQuality ForumYearIndicatorsby Initiative(NQF)AHRQ (SafetyRolling20100% All PayerTeachingStandards)YearIndicatorsHospital>500 COTHLeapfrogBi-AMI-1Commercial/StateannuallyCABG-6Private PayersParticipationAAA-2(Fortune 500)(12 NJNeonatal-Hospitals)1PCI-2The indicators are measures of quality based on patient treatment information. The project relates to medical conditions, such as heart attack or pneumonia. The comparator represents the scope or geographic participation with the metrics.
For healthcare providers, such as hospitals, meeting these quality reports involves laborious chart abstraction by highly qualified (and highly paid) nurses or other clinical experts. Unfortunately, many of the quality metrics (e.g., measures or facts used to determine a measure) are not stored in structured data inside a hospital database. Health care providers accumulate vast stores of clinical information. Clinical information maintained by health care organizations is usually unstructured. Since clinical information is collected to treat patients, the information may contain missing, incorrect, and inconsistent data. Often key outcomes and variables are simply not recorded.
While many health care providers maintain billing information in a relatively structured format, this type of information is limited by insurance company requirements. Billing information generally only captures information needed to process medical claims, and more importantly reflects the “billing view” of the patient, i.e., coding the bill for maximum reimbursement. As a result, billing information often contains inaccurate and missing data from a clinical point of view. Furthermore, billing codes may be incorrect.
Some systems create medical records pursuant to a predetermined structure. The health care provider interacts with the system to input patient information. The patient information is stored in a structured database. However, some physicians may prefer to include unstructured data in the patient record, or unstructured data may have been previously used for a patient.
Given the different approaches to data storage and the likely reliance on unstructured data, deriving quality metrics is expensive and cumbersome. A nurse must find patients who meet inclusion criteria set for these different quality reports, and review the reports by hand to find and enter the criteria. For example, CMS has a voluntary reporting system for several diseases, including heart failure. Every quarter, hospitals identify each heart failure patient that was treated, and fill out a form that is sent to CMS.
One computerized system, CART (CMS Abstraction and Reporting Tool) allows a user to enter in the items in an electronic form. The CART tool then verifies the results by checking for any inconsistencies. The report is sent electronically to CMS. However, a nurse or other clinical expert still manually identifies each patient who is eligible to be in the report, and then manually reviews the medical charts to extract each data point.